It then iterates on every attribute and splits the data into fragments known as subsets to calculate the entropy or the information gain of that attribute. ID3 generates a tree by considering the whole set S as the root node. Distribution of records is done in a recursive manner on the basis of attribute values.ĭifferent libraries of different programming languages use particular default algorithms to build a decision tree but it is quite unclear for a data scientist to understand the difference between the algorithms used.The data taken for training should be wholly considered as root.Discretization of continuous variables is required.See how Data Science, AI and ML are different from each other.ĭespite such simplicity of a decision tree, it holds certain assumptions like: but regression trees are used when the outcome of the data is continuous in nature such as prices, age of a person, length of stay in a hotel, etc. Two Types of Decision TreeĬlassification trees are applied on data when the outcome is discrete in nature or is categorical such as presence or absence of students in a class, a person died or survived, approval of loan etc. The conditions are known as the internal nodes and they split to come to a decision which is known as leaf. In the above representation of a tree, the conditions such as the salary, office location and facilities go on splitting into branches until they come to a decision whether a person should accept or decline the job offer. Learn about other ML algorithms like A* Algorithm and KNN Algorithm. So internally, the algorithm will make a decision tree which will be something like this given below. Let us take a dataset and assume that we are taking a decision tree for building our final model. Still confusing? Let us illustrate this to make it easy. Now the question arises why decision tree? Why not other algorithms? The answer is quite simple as the decision tree gives us amazing results when the data is mostly categorical in nature and depends on conditions. A decision tree is an upside-down tree that makes decisions based on the conditions present in the data. Decision tree algorithm is one such widely used algorithm.
Every machine learning algorithm has its own benefits and reason for implementation.